David Reed (DR): Both of you have rather interesting job titles, so can we start the conversation by unpacking what you are accountable for and how that is supporting the wider goal of democratising data and driving up the data literacy in your organisation?
Kate Jones (KJ): I am responsible for product management, so I have a team of product owners and that is the delivery arm – where we are delivering out – and I am engaging with the business. Additionally, I have got a BI development team, and those data products are the ones that have been moved out in the business, and I have oversight of that. And the third element – almost a side desk, but more working with departments internally and externally – is around leadership and data collection, and that’s probably my favourite bit.
Robbie Priddle (RP): I’m more front of desk. It’s a title I’ve held in two different organisations. Back when I was working in Dyson in 2018, we needed more of the organisation to go on this journey with us – we need more literacy, more awareness, better capabilities across the organisation.
My role is varied in terms of the variety and the different initiatives I get stuck into. We are lucky to have a small team of data enablement specialists, and we have a data Community Manager. The thrust of the team is to examine building more data culture and raising the floor in terms of maturity for how we use data across the organisation. Some of it is very technology focused – how to use our platform, how to upscale, how to best use our tools and technologies. But also, around how do we do community management? How do we create a community of practice for data people?
DR: Let’s break this down further and use the classic lens of people, process, technology. For each of you, was there a particular tech trigger, a moment that the business was starting to do something – perhaps digital transformation – that led the organisation towards saying, “Do you know what we need to do better?”
KJ: We’re wanting to modernise our entire data ecosystem, so there are lots of people moving onto the cloud. We have lots of legacy systems, so we recently retired Business Objects. To get the most out of that data ecosystem, you must have the right data culture. There’s no point investing that time and effort in building those new things if you don’t have an audience out there who really can learn to use them and learn how to how to get the best out of those data products.
There were some challenges in retiring something that people quite familiar with. I think with Business Objects, it was something that had been planned to be removed over several years, and we did finally do it. But there were people hanging on to do their reports. I think a lot of it was bringing people on the journey, building that trust, and really staying with them to make sure that what we deliver did give them the data and the insight that they needed.
I think also, as much as we would have liked to have gone straight from Business Objects to our whole suite of Power BI dashboards, we did take a realistic and pragmatic approach.
DR: This is interesting because people often hear “I’m losing something” rather than “I’m gaining something” and that can get the defences up. Did Specsavers have a tech strategy that made this essential change easier?
RP: I think, much like Coventry Building Society experienced, it is important to push new platforms for smart decision making in terms of what our tech stack looks like. But I would say the bigger driving factor was how to mature the organisation, and you cannot do that on an island – it must be done across the organisation.
You could argue that they are both coming from the same angle: how we mature our data capabilities, and how we become a data-driven organisation from the perspective of a 40-year-old business. I’m sure there are people in the room right now that work for older organisations that agree you need the right technology, but you need to fundamentally change the mindset.
What I was then trying to say is “we have invested in this technology and now we need to upskill everything.” The whole organisation needs to grow and change. So, through our presentations that talked about processes and making them data oriented we addressed notes such as explaining a data literate process and using examples of pointing to something to then explain how it could be so much better if we used the evidence, this data product, this model, or piece of insight.
KJ: When I joined and started speaking to stakeholders across the organisation, I think the common theme was the inability to get to the data and quickly. That was certainly what the CEO said to me.
I think, to me, being able to get to the data that we need quickly and to be able to make those decisions. Are people using data in the meetings to make those decisions? I’m sure we have all seen the monolithic presentation packs that a whole machine of people feed into and you could replace that with a dashboard that is much more insightful and reactive – but in order for that to happen there needs to be backing of all the people on the journey to gather that data, and ultimately, the people who are making those decisions. It is culture at all levels and at all points.
In order for something to be a product it needs to be useful. We want to deliver things that create value in the organisation. I think the other thing that happened in the organisation, as well as this aspiration to be data driven, was around data becoming a principal risk. People want to do all the data, but they also need to minimise the risk that they have in their areas. For example, if they have got manual processes, there’s a benefit to them to come with us on a journey of automation and moving those data management activities out of their teams.
DR: What is your approach to communicate to the organisations that once enabled with data that processes will be easier to follow, quicker, and more effective? How have you communicated that this is for their benefit?
RP: Showing what is possible and how it is possible, I think, is a big one, and coming after a presenting different use cases. For example, if the data literate process is correct, then you shouldn’t see it – it should be a seamless process.
A big part of what we’re trying to go after is that there is a lot of value to be had on advanced analytics – outstanding insights, group reporting, data science, AI, and more. And a problem with immature and legacy organisations is that there are just so many processes all over the place. There are processes which are held up by someone getting an email from someone doing something and they’re invariably using data which now resides in our data platform.
By taking the opportunity to go after some of those processes – which might not be exciting – they can demonstrate change for the organisation, and it makes everyone understand that the use of data should be a key part of everyone’s role. We should aim to drive out this expectation that manual processes are okay.
DR: Let’s think about people as it is usually the biggest challenge for anything that anybody’s trying to attempt around data. Clearly, one of the key aspects of effectively landing a data literacy programme is understanding the personas. I think both of you looked at that and had some interesting insights.
KJ: We have adopted an interesting naming convention for our personas. By working with our L and D colleagues, we came up with four personas: data sceptic, data dreamer, data knight and data wizard, with a definition around people’s current feelings around data, and how they currently use data within a role.
We created a survey that went out and over 20% of the organisation responded, and we created learning journeys based on the personas they matched on our learning platform, and then we used that information to target our first learning programme. We’ve got a cohort of 100 learners across all departments and targets, and we have seen those identified as data sceptics wanting to sign up.
We gathered quantitative and qualitative data from the learners. Some of the comments from people explain that they feared data, and now they can see it’s something that they can use in their day-to-day operations. People want to take it further and are considering data apprenticeships.
RP: I agree. Also, we tend to see low pockets of maturity in some areas and high pockets of maturity for others, so it is more about focusing on what data learning needs does this area of the organisation require.
DR: Both of you have put together programmes to support data literacy. How have you set about trying to get the adoption and then to try and track and ensure completion?
RP: I think one of one of the biggest learnings is just how tricky it can be to land a big data literacy programme. One of our learnings – and one of our big successes – is that we have had a lot more success this year by having a very modular approach to data learning that we can plug into other big things which are happening in the organisation.
We can look at what modular build can be put together to support different areas of the business, rather than one big data literacy programme. If your organisation is not asking you for a big data literacy programme, maybe the think about how you can plug behind whatever other snowplows are out there in the organisation and really attach onto them.
KJ: We are doing it progressively. As much as I know about data and how to encourage data culture, I’m not a comms expert, so I worked with the experts to create communications to launch the survey. We had a competition for our marketplace and did a big corporate event where we did a data governance supermarket suite themed activity.
I think then the challenge was keeping pace with setting that level of expectation. We launched data governance and then getting partners in and getting all the connections and getting through all the red tape – it is quite challenging. You know, we’re making progress.
Similarly, with our data ecosystem, we set an expectation that people will have access to all the data and they can fully self-serve through power BI. I call it the “Data Palace” that we’re building. But people can’t wait until we have built that, which is something I learned.
I’m a very enthusiastic and positive person, but I’ve had to reign myself back in a bit, and I think now, with our next cohort of learners, we’re very similar people that will benefit the most from what we’re building with data ecosystems. With the common data sets we can really start demonstrating what people can get from it and put their learning into practice. The way to embed something you have learned is to use it.
DR: Have you got a tip that people could plan for – or at least have in their minds to watch out for – so that they don’t get completely disrupted if it occurs?
KJ: Find those people and those partners that will help you deliver it. It is not just one person attempting this: it’s L and D, it’s comms, and it’s external partners as well. It is about creating that coalition to deliver.
One of the challenges being in that coalition is that those people also have other priorities as well – you need to remember that they won’t necessarily work to you. Examine how you work and introduce a way that’s effective to get the support that you need when you need it.
RP: For me, it would be keeping an eye on whether you are going too vanilla, too wide, or too big with your plans. It’s a journey we all have to go on.
Make sure you make it personalised to where you are and the needs of your organisation. When I say make it personalised, I don’t mean rewrite the book – a lot of what we are preaching and teaching are concepts and common sense – but really personalise it to address what your organisation needs. Examine what is happening in your organisation and how you can tap into that, rather than trying to come up with a big programme that’s just ticking all the boxes.
To get involved with unique member only discussions that examine topics such as AI investment and securing data change, click here.
To showcase your AI transformation and earn the recognition you deserve, enter the DataIQ Data & AI Awards.
